Mergers and acquisitions are a natural part of the manufacturing economic cycle. Because manufacturing is a broad category composed of many cyclical industries, firms naturally experience periods of consolidation and fragmentation. Additionally, any manufacturer that has years of success reaches some limit of scale. When organic growth rates taper, management can look outwards for opportunities. From a value perspective, the practice of M&A in manufacturing has many good theoretical justifications, too. When consolidating, horizontal integration provides a way for manufacturing firms to optimize production for different geographical areas, improve their negotiating power with suppliers, and realize economies of scale. Manufacturing is also an activity that always exists in a complex supply chain, so vertical integration provides a way for manufacturers to get better control of volatile inputs. As with any M&A, there is also always the prospect of realized synergies through reduced overhead, combined best practices, and better intelligence through more data. In practice, however, M&A in manufacturing fails to create value around 40% of the time. While dysfunction can arise in many ways, on the operational side, a central point of post-M&A pain is systems integration.
The integration friction
For the manufacturing operations team, integration is a difficult but inevitable problem following a merger.
- Every firm acquired is going to have its own ERP instances, MOM systems, WMS systems, and so on. Even if the vendors are the exact same (and they often aren’t), they all have different data models of production. And if you acquire a firm that has also done its own acquisitions, you may have an even more complex landscape to deal with. We recently spoke with someone whose consolidated company had over 30 ERPs! While that’s extreme, finding a number around ten isn’t all that uncommon.
- The people who perform the deal are not the people who integrate the systems. While the challenge of integration is increasingly recognized both by firms and their consultants, the integration process always remains a key uncertainty, one that often remains untested until a year or more after the deal is first inked.
- The domain of manufacturing is itself full of complex, specialized systems. Many of the lower-level systems connect to the physical operations involving production, inventory, quality, and maintenance. These specialized applications and protocols introduce a high degree of data heterogeneity from the source systems.
- Many systems in the manufacturing landscape are mission critical. Downtime and performance degradation are unacceptable, so the system architecture requires high availability and throughput.
- Without standardization, every new system increases complexity by a greater degree than the last, like a pernicious debt.
- If all this data is actually to be integrated, it must be connected in a unified model. Full unification and visibility are the only way to realize the full value of synergies hidden in the “digital gold” of data from diverse systems.
For all these challenges, integration of manufacturing systems is a complex, high-risk endeavor. It succeeds only when the integrators approach the problem with a clear plan about the overall systems architecture and data model. To achieve this success, we know of only one approach that is rapid and reproducible: the Manufacturing Data Hub. Before we get to the benefit of the data hub, however, let’s hear what the most successful M&A practitioners of all time has to say about the need for standardization.
How elite practitioners view standardization
Brad Jacobs is one of the most successful industrialists of all time, In over 4 decades, he’s founded 4 companies that all grew into multi-billion-dollar businesses. While Jacobs’ focus has not been on manufacturing explicitly, his experience is deeply relevant to this article, because a large part of his success came through prolific M&A. Over his career, Jacobs has done over 500 deals. To do this without setting shareholder money on fire is an achievement; to create 10-100x returns from this approach is a singular feat. The key to this success is a rigorous process. And as it turns out, standardization of IT systems is a key part of the process. Here’s Jacobs speaking on M&A standardization in a 2022 podcast with McKinsey:
Andy West [McKinsey]: What about integration? Technology has been a big part of the XPO strategy. How did you optimize the technology infrastructure in the course of integrations?
Brad Jacobs: Two words: fast and standardized…You get cleaner and faster numbers, and you can share information. On some aspects of integration, you want to be gentle [Jacobs refers to cultural aspects], but on integrating technology platforms, you want to rip off the Band-Aid because you’re not functioning at full level until then.
For manufacturing operation data, standardization is a particular challenge. Being challenging, though, means that its success produces far greater differentiation against the competitive field. The data hub, backed by a standardized model of ISA-95, provides the only reproducible template. And, while it’s true that the best time to standardize is right away, don’t feel bad if you find your manufacturing operation is currently a data “Frankenstein”. If the best time to standardize was yesterday, the second best time is surely today.
Why M&A with a data hub
To walk through why a hub is the best path for M&A manufacturing integration, I’ll reference its four key functions, as documented in One Hub, Four Core Functions. In short:
- The Data Consolidation function standardizes all operational data in a unified model.
- The Orchestration function decouples communication between different systems and protocols, reducing the complexity.
- The Interactive function provides a backend to replace, create, and extend MOM functions with custom frontends while preserving data quality and integrity on the backend.
- The Modernizing function provides a way to replace the most deeply embedded legacy systems with minimal disruption.
With that framing in mind, I’ll now look at how these functions specifically apply to M&A integration.
Pre-built integration model
A huge amount of complexity from integration comes with rewriting schemas to accommodate new standards. The only way around this issue is to integrate data using an ontology that represents the entire operation. Without a standard schema, every additional system requires either introducing breaking changes to evolve the schema for new models, or reducing the information of the integrated system by forcing it into the existing schema. The data hub takes an entirely different approach; rather than modifying any model, it translates the target systems into its standard representation. Based on ISA-95, the heart of the data hub is a standard ontology of manufacturing that provides a way to store and relate all data from the manufacturing operation. ISA-95 is the key to all standardization, since it is the most complete and battle-tested representation of manufacturing that exists. Building a custom ontology from scratch is a far harder task, as we discussed in our article Reframing Perspectives on ISA-95.
Multi-directional integration
When the IT and OT teams of the merging firms begin to meet, an inevitable point of long discussions and whiteboarding sessions will be about how to bridge systems that have different responsibilities. Once again, ISA-95 provides an exceptionally useful place to harmonize. The standard is specifically designed to be comprehensive enough to integrate manufacturing data from all levels, including:
- Business-level data, like ERPs, BoMs, and PLMs.
- Operational-level data, like MES systems, WMS, and quality management and testing
- Event-level data emitted from OPC-UA
Having all systems integrate without needing to be “aware” of anything but the standard model also decouples communication and ingestion. This leads to the next benefit: modernization and incremental replacement.
Incrementally modernize
Manufacturing applications are typically not a place where people embrace the “move fast and break things” philosophy. Rather, the difficulty of changes causes many firms to embrace the “if it ain’t broke don’t fix it” mentality. For that reason, legacy systems often live for a long time. These systems pose particular risks for complex integration (like those that arise from M&A). The data hub approach explicitly rejects the need for wholesale “rip and replace.” Rather, it adopts the “strangler fig” pattern, in which new systems incrementally wrap around the legacy systems until the legacy system becomes no longer needed. Since the data hub approach provides a backend to develop any event-driven manufacturing operations system, you can use it to incrementally replace existing legacy screens with a new, modernized backend. In fact, when starting projects with new customers, our first deliverable is sometimes to exactly replicate the legacy screen. This way, the old backend is replaced with a data hub, but the operator experiences no UX disruption from a sudden application change. This incremental approach applies to every aspect of the data-hub integration. While standardization always pays benefits early, it doesn’t need to happen all at once. A principled approach to integration chooses points strategically and incrementally, gradually extending instances to test and modify what works. This also reduces the cost of early problems. Each incremental success makes subsequent integrations easier, since resource models overlap and use cases stack to pay compound interest. For details on our approach to legacy integration, read Coexist and Replace: The MDH as a Legacy Integrator.
Total system of record for the entire operation
In the last three sections, we discussed how the data hub standardizes and decouples storage and exchange over all systems in the operation landscape, even legacy ones. This section emphasizes the end result: complete, unified storage of all resources and events across the entire integrated landscape. Centralizing data in a single, standardized hub results in an application architecture that supports innovation on the shop floor and deep research in the analytics department. The data hub stores all resources as events in a manufacturing knowledge graph. This knowledge graph provides the means to realize synergies at the deepest level through superior standardization, and visibility and queryability of data at all levels of the production cycle. This brings benefits across many teams, as we have detailed in:
Speaking of cross-team benefits, let’s look at a few more benefits that the data hub brings to the wider organization.
Wider organizational benefits
There are also wider organizational benefits to the MDH.
Governance
Data governance refers to the management and visibility of a data system. One of the key benefits of ISA-95 is that it provides a universal language for all stakeholders to discuss objects in the manufacturing system. Once learned, it is the lingua-franca of the operation. When discussing requirements, schedules, plans, and projects, adopting the Data Hub approach reduces friction among all stakeholders. For details on the governance portion, watch our series of videos on manufacturing ontologies.
The cure for conglomeration
While not as common as it used to be, some manufacturing firms end up as conglomerates of firms that make entirely different products with no overlap of inputs or processes. Even in these cases, the data hub provides a unique advantage in that it readily adapts to completely different manufacturing processes in systems while maintaining the same underlying data model. In fact, the data hub approach gains value as manufacturing complexity scales across sites and products. So all the arguments from this article apply just as well to conglomeration, only at a larger scale.
Integrated data, efficient teams, happy shareholders
M&A in manufacturing will never go away. But the integration pain that follows M&A does not need to exist. Rhize’s Data Hub approach provides a new solution that scales to any size. When you’ve been tasked with a new integration, choose the option that standardizes data, decouples communication, modernizes legacy systems, presents the data in a unified related view, and imbues a new degree of data governance across the organization. Choose the one whose performance scales to the most data-intensive, high-availability environment, and whose architectural approach is ready for even the largest conglomeration. In other words, choose the Rhize Manufacturing Data Hub.